Study And Experiment Of Grid Task Scheduling Algorithm Based In IM-ACO

Grid computing is a type of distributed computing,it's a kind of perfect computing technology,which will be used in the fields of science computing, engineering computing and some other large-scale computing.Grid involves the efficient management of heterogeneous,geographically distributed,and dynamically available resources.It connects with and integrates high speed net-work,computers, large scale databases,sensors and remote devices to provide more resources, functions and services.Because the resources in Grid are distributed,heterogeneous and dynamic,how to schedule tasks in Grid to meet users' requirements is a challenging problem.Task scheduling technology is a part of grid core service technology,according to tasks information and suitable scheduling policy,it submits tasks to the different resources.We can formulate the problem as below:consider m(m={1,2,...,M}independent user jobs on n(n={1,2,...,N}) heterogeneous resources with an objective of minimizing the completion time and utilizing the resources effectively.This problem is a typical NP problem.At present,a number of grid tasks scheduling algorithms,such as genetic algorithm(GA),ant colony algorithm(ACA),simulated annealing algorithm and suffrage algorithm etc,have advantages and disadvantages obviously,which cannot finish optimizing the task scheduling by itself and a strategy that combines IM and ACA is not brought forward.In this paper,the merits and disadvantages as ant colony optimization algorithm and other optimization algorithms are studied firstly.Then a novel algorithm,immune mechanism based ant colony optimization(IM-ACO),is proposed to solve Grid Task Scheduling problems.The methods are discussed as below:1) Parse the environment of grid computing,expatiate the definition,character and structure.2) In order to overcome the phenomenon of premature convergence and complexity of solution in ant colony optimization,the immune mechanism,which can maintain the diversity of population and prevent the degradation of population,was introduced to ant colony optimization.It demonstrated that IM-ACO has well global optimization ability in combinatorial optimization problems.3) Some simulating programs are designed to validate algorithms after finishing the SimGrid study.It compares these algorithms with scheduling algorithm based on ACO algorithm to show their correctness.